The hyperspectral image processing is a technology which collects and interprets detailed spectral information from a scene. Image data for a pixel is represented in hundreds of narrow and adjacent spectral bands, virtually as a continuous spectrum. The spectral range includes infrared, visible and ultra-violet light. Detailed spectral resolution makes hyperspectral image technology a powerful tool for detecting chemical substances, anomalies and camouflaged objects, as well as for target-tracking. Traditional hyperspectral image processing uses hundreds of bands to detect or classify targets. The computational complexity is proportional to the amount of data needs to be processed. Thus, the data reduction and simplified algorithm are very critical for real-time execution. The computational complexity of the Hyperspectral processing can be reduced by exploiting spectral content redundancy so that partial number of bands can be used. However, the amount of data to be processed in the Hyperspectral image processing still large compared to that of typical image processing. There are many approaches for processing hyperspectral image data. Hardware clusters may be a feasible solution because these are used to achieve high performance, high availability or horizontal scaling. The cluster technology can also be used for highly scalable storage and/or data management. These computing resources could be utilized to efficiently process the remotely sensed data before transmission to the ground. Digital signal processors are also suitable for hyperspectral computations because it can be optimized for performing multiply-accumulate operations. It is usually implemented in DSP clusters for parallel processing. Traditional store-and-processing system performance is inadequate for real-time hyperspectral image processing without data reduction.
While conventional image pictures are represented by 2 dimensional matrices, the hyperspectral image has one more dimension for band spectral data as shown in FIG. 1. Collected data by hyperspectral image sensors are kept as one cube and each pixel which is located at (x; y) has Nz bands. Notations Nx and Ny are used for indicating total size of pixels in accordance to the axis. Implementing high performance for detection in hyperspectral images is a big challenge because of large number of spectral bands. The Hyperspectral image processing involves three key stages: Preprocessing, Processing, and Post-processing. The overall operation is illustrated in FIG. 2. A Hyperspectral sensor is an array of detectors where a detector collects a spectrum content in a pixel. The spectrum contents from sensors are stored in a cube memory structure as raw image data as shown in FIG. 2. The raw image data is calibrated by the Preprocessing. Each cube contains many numbers of bands which represents the characteristics of a target material. In the Processing, target images are detected by isolating the portion of data while it is highly correlated with the target library. The target library contains spectral information about the object that it is intended to detect. The objective of the Processing is to find out the target image from input cubes that correlates with spectral information stored in the target library. The third step is the Post-processing where actual detected images are displayed with RGB.
The main challenge of general hyperspectral image processing is the backside of its advantages: high volume and complexity of hyperspectral image data. For real-time processing, the complexity should be reduced. The easiest approach is to reduce the number of bands and the amount of library for processing. However, such reductions may eliminate the merit of the hyperspectral image processing. If certain bands have more characteristics to represent the object, all spectrums of bands do not need to detect the target. Thus, our approach determines which bands are more effective for the target detection and then use them to detect targets.